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It’s actually fun to keep modifying the picture, playing with it until you feel it is complete. People involved start thinking about what they want and need instead of what they think is possible. This is the real power of drawing the metric.

Identifying the Information, Measures, and Data Needed

Only after you have a complete picture do you address the components. This picture is an abstract representation of the answer(s) to our root question. It’s like an artist’s rendition for the design of a cathedral—the kind used in marketing the idea to financial backers. When you present the idea to potential donors, you don’t need to provide them with blueprints, you need to pitch the concept.

Next are the specific design elements to ensure the building will be feasible. As the architect, you can provide the artist’s conception and do so while knowing from experience whether the concept is sound. Your next step is to determine what will go into the specific design—the types of structures, wiring, plumbing, and load-bearing walls. Then you will have to determine the materials you need to make it a reality—what do we need to fill in the metric?

Let’s look at the workload example. How do we divide our team’s workload to be the most productive? Remember, the picture is of drink cups—various sizes from 20-ounce, to 32-ounce, to a super-sized 44-ounce. Each cup has a mark that designates the “fill” level—and if we fill above this line, the froth will overflow the cup. Using the picture, we need to determine the following:

How do we measure our team’s level of productivity?

How do we currently allocate (divide) the work?

What are other ways to allocate the work?

Of the three pieces of information listed, only the first seems to need measures. The other two are process definitions. Since our question is driven by a goal (to improve the team’s productivity), the process for designing the metric will produce other useful elements toward the goal’s achievement.

Information can be made up of other information, measures, and data. It isn’t important to delineate each component—what’s important is to work from the complex to the simple without rushing. Don’t jump to the data!

An example of how you can move from a question to measures and then to data follows.

How do we measure our team’s level of productivity? How much can each worker do? Worker A?

Worker B?

Worker C?

How much does each worker do? By worker (same breakout as the previous measure)

How much does each worker have in his or her cup? By worker

How long does it take to perform a task? By worker

By type of task

By task

I logged a sub-bullet for each worker to stress what seems to be anti-intuitive to many people—most times there is no “standard” for everyone. When developing measures, I find it fascinating how many clients want to set a number that they think will work for everyone.

Machinery, even manufactured to painstakingly precise standards, doesn’t function identically. Why do we think that humans—the most complex living organism known, and with beautiful variety—would fit a standardized behavior pattern?

Of course it would be easier if we had a standard—as in the amount of work that can be done by a programmer and the amount of work each programmer does. But this is unlikely.

You may also be curious why we have the first and second measures—how much a worker can do and how much he accomplishes. But since the goal is to increase the productivity of the team, the answer may not be in reallocating the load—it may be finding ways to get people to work to their potential. A simpler reason is that we don’t know if each worker is being given as much as they can do—or too much.

Looking back at Figure 2-1, we may need to decide if the flavor of drink matters. Do we need to know the type of work each worker has to do? Does the complexity of the work matter? Does the customer matter? Does the purpose of the work matter? Does the quality of the work matter? Should we only be measuring around the manager’s assigned work?